Our overall goal is to develop a novel computing solution for automatic and accurate registration (spatial alignment) of three-dimensional (3D) medical images of any modality and any anatomy (rigid or deformable) in 1 minute. Such capability currently does not exist. Existing image registration solutions have limited accuracy and/or limited applicability, preventing wide and routine clinical use. Building on significant prior academic research, we will demonstrate the feasibility of creating the proposed technology in this 1-year STTR Phase I project. After demonstrating feasibility, we will create a fully functional prototype}a compact, relatively low- cost (manufacturing cost: ~$20,000) PC board}of hardware-accelerated image registration, with commercialization as the ultimate goal. Image registration is a fundamental need in modern medicine}a need that remains unmet. It is the necessary first step before images with complementary information can be fused, or images taken at different times can be subtracted to quantify anatomic/physiologic changes. Image registration has numerous other applications, including the registration of pre- and intraoperative images in a host of emerging minimally invasive image-guided interventions. Through system-level simulation and performance characterization (our 2 specific aims), we will create a software simulation of the proposed hardware accelerator in Phase I. We will register existing images of the brain, lungs, and abdomen using this functionally identical and similar-speed simulator to show accurate image registration in 1 min or less. Meeting these performance milestones will provide evidence that this clinically viable and multipurpose computing solution can be created and will justify transitioning to Phase II and continued funding. Our proposed low-cost, ultrafast, easy-to-use, and accurate computing solution promises to unlock the full potential of medical image registration. PROJECT HEALTH RELEVANCE: Combining 2 or more medical images of different types gives more precise information on a patient's condition. Comparing images taken at different times helps monitor how a disease is responding to treatment. In either case, image registration (alignment) is the crucial first step. Current image registration methods are slow, complex, and tedious, with limited practical applicability. We propose developing automatic, high-speed, three-dimensional registration capabilities that are applicable to most organs and image types. We will demonstrate the feasibility of creating such a technology in Phase I before its full development planned for Phase II. } [unreadable] [unreadable] [unreadable]